No-reference video quality assessment by HEVC codec analysis

This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding (HEVC). The proposed assessment can be performed either as a Bitstream-Based (BB) method or as a Pixel-Based (PB). It extracts or estimates the tra...

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Published in2015 Visual Communications and Image Processing (VCIP) pp. 1 - 4
Main Authors Xin Huang, Sogaard, Jacob, Forchhammer, Soren
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2015
Subjects
Elastic Net
HEVC analysis
Machine Learning
No-Reference
Video Quality Assessment
Online AccessGet full text
DOI10.1109/VCIP.2015.7457853

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Abstract This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding (HEVC). The proposed assessment can be performed either as a Bitstream-Based (BB) method or as a Pixel-Based (PB). It extracts or estimates the transform coefficients, estimates the distortion, and assesses the video quality. The proposed scheme generates VQA features based on Intra coded frames, and then maps features using an Elastic Net to predict subjective video quality. A set of HEVC coded 4K UHD sequences are tested. Results show that the quality scores computed by the proposed method are highly correlated with the subjective assessment.
AbstractList This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding (HEVC). The proposed assessment can be performed either as a Bitstream-Based (BB) method or as a Pixel-Based (PB). It extracts or estimates the transform coefficients, estimates the distortion, and assesses the video quality. The proposed scheme generates VQA features based on Intra coded frames, and then maps features using an Elastic Net to predict subjective video quality. A set of HEVC coded 4K UHD sequences are tested. Results show that the quality scores computed by the proposed method are highly correlated with the subjective assessment.
Author Xin Huang
Sogaard, Jacob
Forchhammer, Soren
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  surname: Forchhammer
  fullname: Forchhammer, Soren
  organization: DTU Fotonik, Tech. Univ. of Denmark, Lyngby, Denmark
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Snippet This paper proposes a No-Reference (NR) Video Quality Assessment (VQA) method for videos subject to the distortion given by High Efficiency Video Coding...
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StartPage 1
SubjectTerms Elastic Net
HEVC analysis
Machine Learning
No-Reference
Video Quality Assessment
Title No-reference video quality assessment by HEVC codec analysis
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